Method for generating multiple focuses at different locations

Abstract

A first aspect of the present disclosure relates to a computer-based method for measuring a plurality of optical focuses, comprising the steps of: measuring a first focus; obtaining one or both of the following: a) a focus range for measuring a second focus, where the focus range is based on the first focus; b) measuring a starting value for the second focus, the starting value being based on the first focus; measuring the second focus using the obtained focus range and / or initial value; discarding the second focus if the second focus is measured as an extremum of the focus range; the third focus is measured.

Description

Technical Field

[0001] The present disclosure relates to a computer-based method for measuring a plurality of optical focuses. In addition, a medical device configured to measure a plurality of optical focuses is disclosed. Background Art

[0002] Autofocus (e.g. in microscopy) is a function that allows the focus of an objective to be adjusted automatically to ensure that the image is clear and legible.

[0003] In the case of acquiring images at multiple locations or over a large area, the corresponding focus levels of the locations may be different. Prior to image acquisition, the focus levels associated with the locations can be identified and stored in a focus map. However, adjusting to the proper focus can be difficult. For example, stains on the sample holder or holes in the sample can cause the focus to be adjusted incorrectly, or the focus cannot be identified at all. Therefore, improvements are needed. Summary of the invention

[0004] The present disclosure aims to improve autofocus systems.

[0005] This object is achieved by the disclosed embodiments, which are particularly defined by the subject matter of the independent claims. The dependent claims provide information for further embodiments. Various aspects and embodiments of these aspects are also disclosed in the following summary and detailed description, providing additional features and advantages.

[0006] A first aspect of the present disclosure relates to a computer-based method for measuring a plurality of optical focuses, comprising the following steps:

[0007] - measuring the first focus;

[0008] - Obtain one or both of the following:

[0009] a) a focus range for measuring a second focus, wherein the focus range is based on the first focus;

[0010] b) a starting value for measuring a second focus, wherein the starting value is based on the first focus;

[0011] - measuring a second focus using the obtained focus range and / or starting value;

[0012] - if the second focus is measured as an extreme value of the focus range, discarding the second focus;

[0013] -Measure the third focus.

[0014] Optical focusing can be composed of various devices. Optical focusing can be a focusing system that works with electromagnetic radiation (e.g., light, ultraviolet light). Medical devices and / or diagnostic devices (e.g., microscopes) can include optical focusing. Optical focusing can also be composed of an endoscopic system or an ophthalmoscope system. Laser systems (e.g., laser surgery systems) can also include optical focusing. Cameras or video camera systems have optical focusing as well. Optical focusing can be based on measured images and / or measured reflections, such as reflections of laser beams. Optical focusing can be measured based on parameters such as contrast, clarity, phase, light, projected light, wavefronts, known structures, and / or combinations thereof. Additionally or alternatively, optical focusing can be based on parameters such as focal length, sample-to-lens distance, and / or adjusted lens aperture. Focus can be a point in the depth of field, which is the range of distances in the scene that appear to be acceptable for focus on the image.

[0015] The first focus and the second focus are optical focus values ​​measured at different positions. In the case of acquiring focus along the z direction, the position may be different (e.g., along the x direction and / or along the y direction). Focuses at different positions, such as the first focus and the second focus, may be acquired within a sample. Additionally or alternatively, focuses at different positions may be acquired for different samples. For example, different focuses may be acquired from different holes of a multi-well plate. In particular, in a series of focuses, the second focus may be a focus measured after the first focus (i.e., later than the first focus). In particular, the second focus may be a focus that is continuous with the first focus.

[0016] The focus range may be a range within which the focus is searched. The focus range may be defined as a one-dimensional space, in particular a linear space, having a maximum value and a minimum value. For example, if contrast is used as a parameter for defining the focus, the focus range may be from a minimum contrast value to a maximum contrast value. If multiple parameters are used for the focus measurement, the focus range may be defined as a corresponding multidimensional space, each parameter on which the focus is based having a maximum value and a minimum value. Additionally or alternatively, in the case of using multiple parameters, the focus range may be a one-dimensional range based on a function / mapping of the individual parameters to the one-dimensional focus range. In the case where the second focus is not based on the focus range of the first focus, a predetermined focus range and / or a technically possible focus range may be used for the second focus.

[0017] The starting value of the second focus is the value at which the autofocus system starts searching for focus within a given focus range (eg, a provided focus range or a technically possible focus range). For a multi-dimensional focus range, a starting value may be provided for each dimension of the focus range.

[0018] The extreme value may be a minimum or maximum value of the focus range. Additionally or alternatively, the extreme value may be a value in a predetermined range or, for example, in a 5% segment above or below the focus range. Additionally or alternatively, the extreme value may also be a value that is not within the focus range. Such an extreme value may also be referred to as an "outlier". If the measured / calculated focus also exceeds the predetermined focus range, an outlier may be recorded. In the case where multiple focuses are measured within the same focus range, a statistical analysis may also be performed on the second focus. In such a case, the extreme value may, for example, be based on a predetermined z-score (deviation from the mean) and / or a predetermined percentile (e.g., outside the range of -3σ to +3σ).

[0019] Extreme values ​​can also be content-based. In the case of using classification to find objects within a focus range, the classified objects can be mapped to classification values. For example, objects classified as interesting can be assigned to values ​​within a first value range, while objects classified as uninteresting (e.g., dirt) can be assigned to values ​​within a second value range. In this case, the values ​​of the second value range can be determined as extreme values.

[0020] After checking the first focus and declaring it valid, the information from the first focus (i.e., the focus range and / or focus value) can be used for the second focus (as a focus range or starting value), especially if the first focus is not measured as an extreme value of its focus range. If the second focus is not measured as an extreme value, the second focus is not discarded. The second focus can then become the first value for the subsequent focus.

[0021] Based on the method according to the first aspect, malicious focusing due to a single erroneous focusing can be avoided, especially during the acquisition of the focusing map.

[0022] Based on the method according to the first aspect, a focus map can be automatically derived before starting an experiment / analysis. A focus map can be a representation that indicates different levels of focus and / or focus ranges in a sample area. Therefore, based on the method of the first aspect, a focus map can be automatically generated by incorporating sample knowledge (cells, tissues, etc.) into it so that the correct structure can be reliably focused on in subsequent experiments.

[0023] Embodiments of the first aspect relate to a method for measuring a plurality of optical focuses, wherein a first focus and a second focus and / or a third focus are different in a well of a multi-well plate.

[0024] A multiwell plate, also called a microplate or microplate, can be a flat plate with multiple small wells or compartments arranged in a grid pattern. Multiwell plates can be used in laboratories to perform various experiments and assays in a high-throughput manner. Depending on the specific application and the size of the plate, the number of wells on the plate can range from a few to hundreds, including 6-well, 12-well, 24-well, 48-well, 96-well, 384-well, and even higher well counts. The wells can be designed to accommodate the sample to be analyzed. Each well can have a position on the multiwell plate defined by its row and its column.

[0025] During the method according to the first aspect, a plurality of focuses may be obtained for a plurality of wells, in particular for each well, of the multiwell plate. Thus, the wells may be processed sequentially, i.e. a focus may be obtained for each well. For example, the processing may be performed row by row, column by column. Additionally or alternatively, the processing may be performed in a meander mode (e.g. Figure 1 shown).

[0026] The first focus, the second focus and the third focus may be obtained successively. The second focus may be obtained directly after the first focus. Therefore, the hole of the second focus may be a hole adjacent to the hole of the first focus. In row-by-row or column-by-column processing, if the first focus is obtained for the hole at the end of the previous row / column, the second focus may be obtained for the first hole of the first row / column.

[0027] Thereby, focusing information of different wells, ie focusing information of independent samples, can be collected.

[0028] Embodiments of the first aspect relate to a method for measuring multiple optical focuses, wherein within a single sample, in particular a tissue sample and / or a liquid sample, a first focus and a second focus and / or a third focus differ in a position perpendicular to the focus dimension.

[0029] For the previous embodiments, this may involve arranging one or more optical elements (such as lenses or mirrors) in such a way that they can focus on different planes within the sample. The positions of the first focus, the second focus and / or the third focus may be predetermined and in particular evenly spaced from each other. Additionally or alternatively, the positions of the different focuses may depend on one or more previous focuses.

[0030] Embodiments of a first aspect relate to a method for measuring a plurality of optical focuses,

[0031] The following steps are involved:

[0032] - identifying one or more predetermined structures; and

[0033] Wherein, the first focus, the second focus and / or the third focus are based on one or more predetermined structures.

[0034] The predetermined structure may be provided by a database and / or based on abstract structural information, such as color, height, wavelength, etc. Different focuses can be directed to different predetermined structures. Furthermore, different structures may be identified based on the assumption that they are located in similar or even the same focal plane. Basing the first, second or third focus on the predetermined structure ensures that the obtained focus is from the structure of interest and not from, for example, dirt.

[0035] Embodiments of a first aspect relate to a method for measuring a plurality of optical focuses,

[0036] The following steps are involved:

[0037] - identifying one or more predetermined structures; and

[0038] Therein, the focusing range is based on a predetermined structure.

[0039] If the identified structure has a certain height in the dimension of the optical focus, the upper and lower boundaries of the identified structure can be used as the focus range of the second focus. In this case, the second focus can be searched within the boundaries of the previously captured structure. Thereby, the search for the second focus can be accelerated.

[0040] In addition, if a certain structure is identified, a focus range that is typical for the identified structure can be used. This focus range can then also be used for the second focus. This may also speed up the acquisition of the second focus.

[0041] In a further alternative embodiment, information about one or more acceptable predetermined structures may be provided as an alternative to the focus range or starting value based on the first focus. And, if no predetermined structure is found within the searched focus range, the second focus may be discarded. For this embodiment, it is not necessary to measure the focus extremes.

[0042] Embodiments of a first aspect relate to a method for measuring a plurality of optical focuses,

[0043] In this case, one or more predetermined structures are identified based on a classifier, in particular a machine learning algorithm.

[0044] The classifier can be any classifier for image classification. The classifier can be based on statistical methods and / or machine learning methods.

[0045] Classifiers can be based on methods without a training phase, such as:

[0046] - histogram-based methods, which are applied, for example, to pixel intensity values ​​or color distribution as features;

[0047] - a texture analysis method configured to analyze texture patterns in an image;

[0048] - shape-based methods, which are configured to detect predetermined shapes, such as edges and / or ratios of bounding box sizes;

[0049] - Template matching methods, which are configured to search for predetermined patterns and / or configurations within the image;

[0050] - scale-invariant feature transform methods, which are configured to extract unique features from images that are not affected by scale, rotation, and illumination changes;

[0051] - component analysis methods, which segment an image into connected regions based on pixel values ​​or other image attributes;

[0052] - A statistical method configured to identify statistical measures from the image data, such as mean, standard deviation, skewness and / or kurtosis.

[0053] Non-learning classifiers can be used in scenarios where labeled training data is limited and / or computational efficiency is required.

[0054] Machine learning based classifiers may include, for example, one or more of the following methods:

[0055] - a neural network, in particular a convolutional neural network configured to recognize certain patterns in images;

[0056] - a support vector machine, which is specially configured to separate different data sets in the acquired images;

[0057] - Random Forest;

[0058] - a K-nearest neighbor method, for example, which is configured to evaluate the degree of match between a predetermined image and an obtained image.

[0059] Classification and / or pattern recognition can be accurately obtained based on neural networks. The neural network may include standard neural networks, such as VGG and / or ResNET. VGG is a convolutional neural network architecture that can use a depth stack of 3×3 convolutional filters and convolutional layers. ResNET is a deep convolutional neural network architecture that uses residual connections to achieve training of very deep (hundreds of layers deep) neural networks. In particular, convolutional neural networks can be used for efficient image processing. In order to determine the continuous variables of the scaling, translation and rotation of the searched pattern, a fully connected single-layer or multi-layer recurrent neural network can be used in addition to or instead of another classifier. The recurrent neural network can be a feedforward neural network, such as a multilayer perceptron. In particular, the recurrent neuron network can be configured to obtain its input from the convolutional neural network. Thus, geometric differences can be calculated with high accuracy.

[0060] Embodiments of a first aspect relate to a method for measuring a plurality of optical focuses,

[0061] The center of the focusing range of the second focus is equal to the first focus.

[0062] Centering the focus range of subsequent focuses with the first focus can effectively implement the assumption that the focus can remain constant or at least similar for different focuses. In the case of using a multi-dimensional focus range, at least one of the dimensions of the measured focus can be used to center the corresponding dimension of the subsequent focuses.

[0063] The size of the focus range may, for example, depend on a previous classification of an image / object detected with the first focus. Additionally or alternatively, the size of the focus range may depend on a previously used size, for example, the size of the focus range used for the first focus. Additionally or alternatively, the size of the focus range may be a predetermined size, for example, a size provided by a human user. In case of a multi-dimensional focus range, different sizes may be determined for each focus range.

[0064] Advantageously, by using the information of the first focus, the extrapolation of the discarded second focus can be efficiently achieved.

[0065] Embodiments of a first aspect relate to a method for measuring a plurality of optical focuses,

[0066] The following steps are involved:

[0067] - If the second focus is measured to be at the extreme of the focus range, and the focus range is adjusted before discarding the second focus.

[0068] If the second focus is measured as an extreme value, it may be the case that the focus range is just too small to obtain a proper focus. At least in this case, it makes sense to extend the used focus range before discarding the second focus. The size of the extension may be the same as the used focus range. Additionally or alternatively, the extension may be a fraction of the size of the used focus, such as 10%, 20% or 50%. In case multi-dimensional focusing is used, the extension of a single focus may be different. Additionally or alternatively, the extension may depend on an assessment of how to integrate the focus within the used focus range (i.e. how to make it better). Thus, the focus range may be based on the gradient of a quality function (focus score function) of the change in focus or the change in focus itself within the used focus range. If within the used focus range, the focus integrates quickly but ends at an extreme value, a small extension of the used focus range may be sufficient to obtain the best focus.

[0069] Advantageously, by using focus extension, premature discarding of the second focus can be avoided.

[0070] Embodiments of a first aspect relate to a method for measuring a plurality of optical focuses,

[0071] Therein, the focus range is extended on one side of the focus range, in particular on that side of the focus range at which the second focus has been identified as an extreme value, or on both sides of the focus range.

[0072] Advantageously, by extending the focus range on the side where the extremum occurs as the second focus, the information that the focus may be located in this direction can be efficiently utilized, because it is unlikely that the second focus will be found on the other side. On the other hand, by extending the second focus on both sides, especially equally, a simple focus extension can be achieved.

[0073] Embodiments of a first aspect relate to a method for measuring a plurality of optical focuses,

[0074] The following steps are involved:

[0075] If the second focus is measured as an extreme value of the extended focus range and the focus score of the second focus of the extended focus range is higher than the focus score of the second focus in the non-extended focus range, the focus range is extended again.

[0076] In some cases, the extension of the focus range is too small to obtain a proper second focus (i.e., with an acceptable focus score). In this case, the focus value of the second focus may be an extreme value within the extended focus range. If an extreme value is identified again, it may be beneficial to further extend the initial focus range. Therefore, a second focus range extension may be performed before the extreme value is finally discarded. The second focus range extension may be implemented / determined in the same manner as the first focus range extension, which is described in the previous embodiment.

[0077] For example, based on a focus score (function), the quality of the obtained focus can be measured. The higher the score, the better the focus. Thresholds can indicate when one or more focuses are acceptable. On the focus score, a selection can also be made between acceptable focuses. The focus score can include one or more parameters of the corresponding focus, such as contrast, sharpness, phase, etc. Additionally or alternatively, the focus score can be based on the result of a classifier.

[0078] However, if the focus is not found within the focus range used and in the first extension of the focus used, there may be a risk that the optical system integrates into an undesired focus (e.g. a stain or a piece of dirt in a petri dish). Therefore, additional measurements may help to check the validity of the focus, at least when searching for the focus within the second extended focus range. This can be done, for example, by a classifier (as above), which classifies the object that integrates the second focus (i.e. finally locks on it). If the focused object is not the object of interest, the second focus can be discarded anyway. Of course, a parallel validity check can also be performed on the focus found within the first extended focus or within the non-extended focus.

[0079] Advantageously, if information is determined that a second focus may exist, the second focus does not have to be discarded.

[0080] Embodiments of a first aspect relate to a method for measuring a plurality of optical focuses,

[0081] This includes one or both of the following steps:

[0082] - determining a second focus based on a non-discarded focus before the second focus, in particular the first focus;

[0083] - Based on the non-discarded focuses following the second focus, in particular the third focus, the second focus is determined.

[0084] The discarded second focus value may be replaced with the estimated focus, ie the estimated focus value, in different ways.

[0085] As a first alternative, an extrapolation can be performed. Such an extrapolation can use the knowledge of the focus measurement at another position to estimate the second focus. Such a focus can be a previous focus, for example, the first focus. In this case, the second focus will be replaced by the focus value of the first focus. In addition, the focus range of the first focus can also be assigned to the second focus. This may make sense if the focus range is further inherited to a focus measured later, such as a third focus. In particular, the second focus can be estimated based on the average value of the previous focus. The average value can be weighted using weights, which increase as the distance of the measurement position of the corresponding focus from the second focus position decreases. In this way, the closest focus of the weighted average value of the second focus has the largest weight.

[0086] The extrapolation may also use information from one or more focuses measured after the second focus, e.g., for an estimate of the second focus, a third focus may be used. This type of extrapolation may also be referred to as "backward extrapolation" because one or more measurements are used for the extrapolated value of a previously discarded measurement. (In contrast, the extrapolation described above, i.e., the extrapolation of a subsequently discarded measurement using one or more measurements, may be referred to as "forward extrapolation").

[0087] As a second alternative, interpolation can be performed. Interpolation can be based on forward extrapolation and backward extrapolation. In the case of interpolation, one or more measured values ​​before and after the discarded second focus are used to estimate the second focus. Interpolation or backward extrapolation can be used in particular if a focus map (i.e. the focus map of all positions / wells to be analyzed) is determined and the actual experiment is run afterwards. Of course, the focus map can also be determined by using one or more forward extrapolations on the discarded focuses.

[0088] Advantageously, by using extrapolation or interpolation, the discarded second focus may be estimated effectively and efficiently.

[0089] Embodiments of a first aspect relate to a method for measuring a plurality of optical focuses,

[0090] The following steps are involved:

[0091] - Determining the second focus based on the non-discarded focus measured at the position or hole closest to the second focus.

[0092] Depending on the way the focus is measured at different positions, the measurement sequence may result in consecutive measurements of focuses that are not closest to each other. This may happen, for example, in case row-by-row or column-by-column measurements are used, or the measurements are performed with a random walk. In this case, if a second focus measurement has been discarded, a preceding first focus may be further away from the position of the second focus than another focus measured previously or subsequently. Then, it may be decided to use a (non-discarded) focus measurement at a position that is closer than the preceding first focus position. Such a focus may, for example, be located at a predetermined position next to the discarded second focus position, and / or at an aperture next to the aperture of the discarded second focus. Advantageously, by using focus information at a closer position, the discarded second focus may be estimated based on more relevant information.

[0093] Embodiments of a first aspect relate to a method for measuring a plurality of optical focuses,

[0094] The following steps are involved:

[0095] - obtaining one or more of a starting value or a focus range for measuring a third focus, wherein, if the second focus is discarded, the corresponding parameters are based on a focus preceding the second focus, in particular the first focus.

[0096] If the second focus is discarded, and the focus range and / or starting value of the third focus is to be based on information from the second focus, this information must be provided as an option. In this case, the focus range and / or starting value basis of the third focus can be obtained from the last non-discarded focus.

[0097] For example, the focus range may be directly derived from the first focus, or at least the center point of the focus range of the third focus may be based on the first focus. Furthermore, the focus range of the third focus may be considered as a (weighted) average of the focus ranges of previous non-discarded focuses. And / or, the center point of the focus range of the third focus may be based on a weighted average of previous non-discarded focuses. Thus, advantageously, the focus range of the third focus may be provided even if the second focus is discarded and no estimate for the second focus (which may be considered as, for example, the center point of the focus range of the third focus) is available.

[0098] Embodiments of a first aspect relate to a method for measuring a plurality of optical focuses,

[0099] The following steps are involved:

[0100] - obtaining one or more of a starting value or a focus range for measuring a third focus, wherein if the second focus is discarded, the corresponding parameter is based on a focus subsequent to the second focus.

[0101] Similar to the previous embodiments, in case information from the second focus is not available, information for the third focus can also be provided based on information of one or more subsequent focuses. In an embodiment, a measurement series of all available focuses, such as a focus map, can be completed, and then the missing focus (i.e. the discarded / ignored second focus) can be estimated based on the information of the available focuses, in particular the information of one or more recent focuses, and / or the information of the focuses with the same classification object.

[0102] Therefore, the third focus may be based on forward extrapolation, backward extrapolation and / or interpolation of one or more existing focus values.

[0103] A second aspect of the present disclosure relates to a computer-based method for measuring a plurality of optical focuses of a focus map,

[0104] The following steps are involved:

[0105] - obtaining a plurality of positions, wherein the focus is to be measured at each position;

[0106] - measuring a first focus at the obtained first position;

[0107] - obtaining one or more requirements for a second focus to be measured at a second position, wherein the requirements include at least one of:

[0108] a) First focused information;

[0109] b) information about one or more acceptable objects of the second focus;

[0110] - Discarding the second focus if it is measured to be at the extreme of its focus range and / or if the second focus does not include an acceptable object.

[0111] The definitions, embodiments and features of the embodiments provided in detail for the first aspect may also be applicable to and / or used in the embodiments of the second (or third) aspect.

[0112] A focus map is a map generated for different locations where an experiment or analysis should be performed. The experiment or analysis can be performed simultaneously with the generation of the focus map. Additionally or alternatively, the experiment or analysis can be performed after the focus map is completed. The locations of the focus map can be the locations of different wells of a multi-well plate or the locations within a single sample.

[0113] The requirement for the second focus based on the information of the first focus may be a focus value. The focus value of the first focus may be used as a starting point for the focus search of the second focus. Additionally or alternatively, the requirement for the second focus based on the information of the first focus may be a focus range. Additionally or alternatively, the requirement for the second focus based on the information of the first focus may be an object classification of the first focus. Additionally or alternatively, the requirement for the second focus may be one or more acceptable objects that must be displayed in the focus, such as a certain organelle.

[0114] If the focus value is found to be the extreme value of the focus range of the second focus, and / or if no acceptable object is found in the second focus, the second focus is discarded. In this case, either the focus is not found or the optical system is only focused on an unwanted object, such as a piece of dirt.

[0115] A third aspect of the present disclosure relates to a medical device with an autofocus function.

[0116] It is configured as:

[0117] - performing a method according to any of the preceding claims; or

[0118] - communicating with a device executing the method according to any of the preceding claims, in particular communicating with the device via an application user interface.

[0119] The medical device may also be a laboratory device and / or a diagnostic device. In particular, an application user interface may be arranged so that the medical device exchanges information with a central server, for example in the cloud. In particular, the method according to the first aspect or the second aspect may be operated in the cloud and may in particular cooperate with a plurality of medical devices to adjust their optical focus. BRIEF DESCRIPTION OF THE DRAWINGS

[0120] Further advantages and features are obtained from the following examples, some of which are referred to in the drawings. These figures do not always show the embodiments to scale. The dimensions of the individual components may be enlarged or reduced, in particular for clarity of description. For this reason, these figures are at least partially schematic.

[0121] Figure 1 A multi-well analysis focus map is shown with the second focus discarded according to an embodiment of the present disclosure.

[0122] Figure 2 A single sample analysis focusing diagram is shown with the second focusing discarded according to an embodiment of the present disclosure.

[0123] Figure 3 An expansion of the focus range of the second focus according to an embodiment of the present disclosure is shown.

[0124] Figure 4 A classification function associated with the second focus according to an embodiment of the present disclosure is shown.

[0125] Figure 5 A further classification function related to a second focus according to an embodiment of the present disclosure is shown.

[0126] Figure 6 A further classification function related to a second focus according to an embodiment of the present disclosure is shown.

[0127] Figure 7 A schematic diagram of a microscope system according to an embodiment of the present disclosure is shown.

[0128] In the following description, reference is made to the accompanying drawings which form a part of this disclosure and which show specific aspects that can be understood of the present disclosure. Like reference numerals refer to components that are identical or at least similar in function or structure.

[0129] In general, the disclosure of the described method also applies to the corresponding device (or apparatus) for performing the method or the corresponding system including one or more devices, and vice versa. For example, if a specific method step is described, the corresponding device may include a component for performing the described method step, even if the component is not explicitly described or represented in the figure. On the other hand, for example, if a specific device is described based on a functional unit, the corresponding method may include one or more steps for performing the described function, even if such steps are not explicitly described or represented in the figure. Similarly, the system may be provided with corresponding device components or components for performing specific method steps. Unless otherwise explicitly stated, the features of the various exemplary aspects and embodiments described above or below may be combined. DETAILED DESCRIPTION

[0130] Figure 1 A multi-well analysis focus map system 100 with discarded second focus according to an embodiment of the present disclosure is shown. The goal of the focus map system 100 is to generate a focus map 101 with focus for each well on a multi-well plate. The different wells on the multi-well plate are arranged in columns 1 to 8 and rows A to F, so that each well 102, 104 has a unique position and can be identified by its column and its row. The generation of the focus map is performed sequentially by an optical system (e.g., a microscope (not depicted) including an autofocus device that measures each focus). The optical system focuses on the multi-well plate 101 from above (i.e., from a dimension perpendicular to the dimension generated by rows A to F and columns 1 to 8). In this case, the measurement sequence is a meander sequence 106, 108, with one line sampling in the direction from column 1 to column 8, and the next line sampling in another direction from column 8 to column 1. The meander sequence 106, 108 starts from row A and proceeds to row F.

[0131] In order to efficiently use the information of the measured focus, the focus range of the first focus (which can be basically any focus on the focus map 101) is used for the second focus following the first focus. Additionally or alternatively, the focus value of the first focus is used as the starting value of the second focus. If the focus value of the first focus is used as the starting value of the second focus, the focus range of the first focus cannot be used. In this case, a technically possible focus range can be used.

[0132] A scale is depicted on the left, showing a focus range 112 for a first focus. The focus has an upper limit 114 and a lower limit 116. Between the focus range 112, the focus is scaled linearly with a center point. The first focus is measured at a focus value (or focus level) 118 that is within the focus range 112. Therefore, the focus map system 100 does not discard the focus and assumes that the focus is valid. For example, this can be the focus of the hole (3; B) 102 on the focus map 101, or any other focus indicated by a check mark on the focus map.

[0133] On the right a scale is depicted showing the focus range of the second focus 120. The focus range of the second focus is consistent with the focus range 112 of the first focus. It is inherited from the first focus. Furthermore, the focus value 118 of the first focus is used as the center point of the focus range 120 of the second focus. This means that the second focus starts its measurement from the focus value measured for the first focus. However, the value 122 of the second focus 120 is measured as the upper limit 114 of the second focus range. Therefore, it is assumed that the measured focus is not integrated to provide useful information. This can be, for example, focus (2; B) 104 on the focus diagram 101, or any focus indicated by a cross on the focus diagram. A focus measurement error may affect the remaining focuses. If the next focus routine will start from focus level 122, then the focus may not be found at all. This failure may continue for the rest of the sequence, causing all remaining focuses to be wrong. Therefore, the second focus is discarded. This means that the information obtained from this focus measurement is ignored.

[0134] Figure 2 A focus map system 200 is shown with a single sample analysis focus map 201 discarding the second focus according to an embodiment of the present disclosure. A focus map is a map obtained using, for example, Figure 7 Generated by optical equipment such as a microscope within the microscope system shown.

[0135] Image-based autofocus devices usually search for high-contrast areas but cannot distinguish between real samples or unwanted structures such as dust, dirt, etc. As a result, it may happen that the autofocus routine focuses on the wrong structure.

[0136] To facilitate the experimental procedure, a focus map including positions and focus values ​​at each position can be automatically generated and then (or simultaneously) performed for the experiment / analysis. The focus map positions 102, 104 can be automatically set and the focus level at each position can be generated by using a software autofocus device (image-based autofocus device) or a hardware autofocus device (reflection-based autofocus device). Thus, the focus of the acquired position ("first position") can be used as the starting point for the focus to be measured at another position ("second position"). Additionally or alternatively, the focus range of the first focus (at the first position) can be used as the basis for the focus range of the second focus (at the second position). Additionally or alternatively, if an object is identified for the first focus, this information can also be used to facilitate finding the second focus. In this way, already available information can be used to facilitate focus measurement. Based on the focus map, the experiment / analysis can be performed. No user interaction is required and no unnecessary light dose is applied to the sample.

[0137] The single sample analysis performed in this case is based on a sample 210. The optical system is focused on the sample 210 from above (i.e. from a dimension perpendicular to the pattern dimension). In the sample, multiple focus positions 102 can be analyzed and focus (values) 118 can be measured. However, if there is no sample (e.g. hole 220) at a focus map position (e.g. position 104), the autofocus device will not find focus. In these cases, the autofocus device tends to erroneously take one of the focus range limits as the focus level. The focus routine for the next focus map position will start from the last found wrong focus level. Since this is the limit of the focus range, focus will not be found and thus may be further away from focus at each next focus map position. This makes the automatic focus map generation unreliable. The user must wait until the focus map is completed and all focuses are controlled before manually starting the experiment. Otherwise, erroneous focus map points will result in an inaccurate focus map. Similar problems may occur if an acceptable object is not found in the first focus at a certain position. Then, the subsequent second focus cannot be based on the object found for the first focus.

[0138] In the embodiment shown here, the empty focus (position) is defined as follows:

[0139] (1) Empty focus map positions are positions where the autofocus device detects a focus 120 at the limits of a focus range 112 having an upper limit 114 and a lower limit 116. These focuses (i.e., focuses 104) are then discarded / ignored in the focus map and interpolated from neighboring focuses. Alternatively, following the same procedure, an empty focus map position may be replaced with a new focus map position automatically created nearby.

[0140] (2) Empty focus positions are positions where the trained classifier for the desired structure did not "find" the structure in the images of the focus stack. These focuses are also discarded / ignored. This may result in additional searches above or below the currently used focus range.

[0141] This embodiment allows the user to reliably generate a focused image and a reliable focus map 201 for a single sample 210. Since the user does not have to control all focus map positions, the imaging experiment can be automatically started immediately after the focus map is generated. This can save time that the user must spend on experiments / analysis. The described method minimizes the time points of incorrect focus, resulting in higher quality data.

[0142] Figure 3 3. The expansion function 300 of a second focus according to an embodiment of the present disclosure is shown. On the left, the second focus is depicted with an initial focus range 112. In this embodiment, the focus range 112 is inherited from the first focus measured previously. Alternatively, the focus range 112 may be a standard focus range that is used as the initial focus range for all focuses of a series of focus measurements. The focus range 112 has an upper limit 114 and a lower limit 116.

[0143] As can be seen, the measured second focus (value) 118 is equal to the upper limit of the focus range 112. This indicates that an appropriate focus value for the second focus has not been found. However, before the focus value is discarded, a further measurement is performed. For this further measurement, the focus range 112 is extended. This extended focus range 302 is shown on the right. A focus range extension 304 is provided on the side of the focus range 112 where the focus value is measured as the limit value 114. Based on the extended focus 300, a second measurement is performed. If the measured focus (value) is not a limit value and / or depicts a predetermined acceptable object (such as a specific cell organ), the second focus of the second measurement is verified. Otherwise, the second focus will be discarded.

[0144] Figure 4 A classification function 400 related to the second focus according to an embodiment of the present disclosure is shown. Thus, the focus range extension of the second focus is used in conjunction with the object classification, and the focus extension depends on the result of the object classification.

[0145] The second focus is searched by measurement (i.e., imaging) within the initial focus range 112. The initial focus range has an upper limit 114 and a lower limit 116. Within the initial focus range 112, the optical system can find a first focus value at the focal plane 404a. Object classification is performed simultaneously and all measured images are checked. Object classification can be based on non-learning methods and / or machine learning methods (as described above). Based on the object classification, it is determined that the object 404b with the first found focus value is not an object of interest in the case of the experiment / analysis. This is verified based on images of the object 404b with other focus values. For example, the object 404b of the first focus value is a piece of dirt. Therefore, the focus value is discarded / ignored and the second focus has not yet been found.

[0146] However, the focus range 112 is extended on the lower limit 116 by the focus extension 402. Searching within the extended focus range results in an additional focus value 406a. For this additional focus value 406a, the object classification performed simultaneously produces a positive result. The object 406b at this focus value is the object of interest. Therefore, the second obtained focus value 406a is not discarded / ignored and is used as the second focus. (For clarity, the term "second focus" is used as an identifier. This focus may also be called differently, especially because in this case, the "second focus" is not dependent on the "first focus".)

[0147] Figure 5 A further classification function 500 relating to a second focus according to an embodiment of the present disclosure is shown. Thus, a focus measurement of the second focus is used in conjunction with object classification and a selection is made between two focus value candidates depending on the result of the object classification.

[0148] The second focus is searched by measurement within the initial focus range 112. The initial focus range has an upper limit 114 and a lower limit 116. Within the initial focus range 112, the optical system can find a first focus value at the focal plane 502 and a further focus value at the focal plane 504. The further focus value found at the focal plane 504 is detected as an even better focus value. At the same time, an object classification is performed and the measured focus is checked. Thereby, the object classification determines that the classification score of the second maximum value identified as the first found focus is greater than the classification score of the maximum value identified as the second measured focus. Therefore, the first measured focus is determined to be the second focus.

[0149] Figure 6 A further classification function 600 related to the second focus according to an embodiment of the present disclosure is shown. Thus, the focus measurement of the second focus is used in conjunction with the object classification, and the focus range depends on the result of the object classification.

[0150] The second focus is searched by measuring within the initial focus range 112. The initial focus range has an upper limit 114 and a lower limit 116. The measurement results in several objects 602a that are classified as interesting. These objects are located in the focus range 602b. The rest of the focus range 112 does not include any object of interest that the optical system can focus on. Therefore, the second focus is determined using the focus range 602.

[0151] Some embodiments relate to a microscope comprising a microscope incorporating Figures 1 to 6 Alternatively, the microscope may be combined with Figures 1 to 6 Part of or connected to one or more of the described systems. Figure 7 A schematic diagram of a system 700 for performing the methods described herein is shown. The system 700 includes a microscope 710 and a computer system 720. The microscope 710 is used to capture images and is connected to the computer system 720. The computer system 720 is used to perform at least a portion of the methods described herein. The computer system 720 can be used to execute a machine learning algorithm. The computer system 720 and the microscope 710 can be separate entities, but can also be integrated in a common housing. The computer system 720 can be part of a central processing system of the microscope 710, and / or the computer system 720 can be part of a subcomponent of the microscope 710, such as a sensor, actuator, camera or lighting unit of the microscope 710.

[0152] The computer system 720 may be a local computer device (e.g., a personal computer, laptop, tablet, or mobile phone) having one or more processors and one or more storage devices, or may be a distributed computer system (e.g., a cloud computing system having one or more processors and one or more storage devices, distributed in different locations, for example, at a local client and / or one or more remote server farms and / or data centers). The computer system 720 may include any circuit or combination of circuits. In one embodiment, the computer system 720 may include one or more processors, which may be of any type. As used herein, a processor may represent any type of computing circuit, such as a microscope or a microscope component (e.g., a camera), or any other type of processor or processing circuit, such as, but not limited to, a microprocessor, a microcontroller, a complex instruction set computing (CISC) microprocessor, a reduced instruction set computing (RISC) microprocessor, a very long instruction word (VLIW) microprocessor, a graphics processor, a digital signal processor (DSP), a multi-core processor, a field programmable gate array (FPGA). Other types of circuits that may be included in the computer system 720 may be custom circuits, application specific integrated circuits (ASICs), etc., such as one or more circuits (e.g., communication circuits) for wireless devices such as mobile phones, tablet computers, laptop computers, two-way radios, and similar electronic systems. The computer system 720 may include one or more storage devices, which may include one or more storage elements suitable for a particular application, such as a main memory in the form of a random access memory (RAM), one or more hard disk drives, and / or one or more drives for handling removable media such as compact disks (CDs), flash memory cards, digital video disks (DVDs), etc. The computer system 720 may also include a display device, one or more speakers, and a keyboard and / or controller, which may include a mouse, a trackball, a touch screen, a voice recognition device, or any other device that allows a system user to input information to and receive information from the computer system 720.

[0153] Part or all of the method steps can be performed by (or using) hardware devices, such as processors, microprocessors, programmable computers or electronic circuits. In some embodiments, one or more of some of the most important method steps can be performed by such devices.

[0154] According to certain implementation requirements, embodiments of the present invention may be implemented in hardware or software. It may be implemented using a non-temporary storage medium (e.g., a digital storage medium, such as a floppy disk, DVD, Blu-ray, CD, ROM, PROM, EPROM, EEPROM, or FLASH memory, etc.), in which electronically readable control signals are stored, which are (or can be) coordinated with a programmable computer system to perform the corresponding method. Therefore, the digital storage medium may be computer readable.

[0155] Some embodiments according to the invention comprise a data carrier having electronically readable control signals, which are capable of cooperating with a programmable computer system, such that one of the methods described herein is performed.

[0156] Generally, embodiments of the present invention can be realized as a computer program product with a program code, when the computer program product runs on a computer, the program code is used to perform one of the methods. The program code may, for example, be stored on a machine readable carrier.

[0157] Other embodiments comprise the computer program for performing one of the methods described herein, stored on a machine readable carrier.

[0158] In other words, therefore, one embodiment of the inventive method is a computer program having a program code for performing one of the methods described herein, when the computer program runs on a computer.

[0159] Therefore, another embodiment of the present invention is a storage medium (or a data carrier or a computer-readable medium) having stored thereon a computer program for performing one of the methods described herein when executed by a processor. The data carrier, the digital storage medium or the recorded medium is typically tangible and / or non-transitory. Another embodiment of the present invention is an apparatus as described herein, comprising a processor and a storage medium.

[0160] Therefore, another embodiment of the invention is a data stream or a sequence of signals representing the computer program for performing one of the methods described herein.For example, the data stream or the sequence of signals may be intended for transmission via a data communication connection, for example via the Internet.

[0161] A further embodiment comprises a processing means, for example a computer or a programmable logic device, adapted to perform one of the methods described herein.

[0162] A further embodiment comprises a computer having installed thereon the computer program for performing one of the methods described herein.

[0163] Another embodiment of the invention comprises an apparatus or system configured to transfer (e.g., electronically or optically) a computer program for performing one of the methods described herein to a receiver. The receiver may be, for example, a computer, a mobile device, a storage device, etc. The apparatus or system may comprise, for example, a file server for transferring the computer program to the receiver.

[0164] In some embodiments, a programmable logic device (e.g., a field programmable gate array) can be used to perform some or all of the functions of the methods described herein. In some embodiments, a field programmable gate array can collaborate with a microprocessor to perform one of the methods described herein. Typically, these methods are preferably performed by any hardware device.

[0165] As used herein, the term "and / or" includes any and all combinations of one or more of the associated listed items and may be abbreviated as " / ".

[0166] Although some aspects have been described in the context of an apparatus, it is apparent that these aspects also represent a description of a corresponding method, wherein a block or device corresponds to a method step or a feature of a method step. Similarly, aspects described in the context of a method step also represent a description of a corresponding block or item or feature of a corresponding device.

[0167] Embodiments may be based on the use of machine learning models or machine learning algorithms. Machine learning may refer to algorithms and statistical models that can be used by computer systems that do not use explicit instructions, but rely on models and inferences to perform specific tasks. For example, in machine learning, data transformations inferred from analysis of historical and / or training data may be used instead of rule-based data transformations. For example, a machine learning model may be used or a machine learning algorithm may be used to analyze the content of an image. In order for a machine learning model to analyze the content of an image, a machine learning model may be trained using training images as input and training content information as output. By training a machine learning model using a large number of training images and / or training sequences (e.g., words or sentences) and associated training content information (e.g., labels or annotations), the machine learning model "learns" to recognize the content of the image, so the machine learning model may be used to recognize the content of the image not included in the training data. The same principle applies to other categories of sensor data: by training a machine learning model using training sensor data and expected outputs, the machine learning model "learns" the conversion between sensor data and output, which can be used to provide outputs based on non-training sensor data provided to the machine learning model. The provided data (e.g., sensor data, metadata, and / or image data) may be preprocessed to obtain a feature vector, which is used as an input to the machine learning model.

[0168] The machine learning model can be trained using training input data. The above example uses a training method called "supervised learning". In supervised learning, the machine learning model is trained using multiple training samples, each of which may include multiple input data values ​​and multiple expected output values, i.e., each training sample is associated with an expected output value. By specifying the training samples and the expected output values, the machine learning model "learns" which output value to provide based on input samples similar to the samples provided during training. In addition to supervised learning, semi-supervised learning can also be used. In semi-supervised learning, some training samples lack corresponding expected output values. Supervised learning can be based on supervised learning algorithms (e.g., classification algorithms, regression algorithms, or similarity learning algorithms). When the output is limited to a limited set of values ​​(categorical variables), a classification algorithm can be used, that is, the input is classified into one of the limited set of values. When the output can have any numerical value (within a certain range), a regression algorithm can be used. Similarity learning algorithms can be similar to classification and regression algorithms, but are based on learning from examples using a similarity function that measures the similarity or degree of correlation between two objects. In addition to supervised or semi-supervised learning, unsupervised learning can also be used to train machine learning models. In unsupervised learning, (only) input data may be provided, and an unsupervised learning algorithm may be used to find structure in the input data (e.g., by grouping or clustering the input data, finding commonalities in the data). Clustering is the distribution of input data comprising multiple input values ​​into subsets (clusters) such that input values ​​within the same cluster are similar according to one or more (predetermined) similarity criteria, but are dissimilar to input values ​​included in other clusters.

[0169] Reinforcement learning is the third group of machine learning algorithms. In other words, reinforcement learning can be used to train machine learning models. In reinforcement learning, one or more software actors (called "software agents") are trained to take actions in an environment. Based on the actions taken, a reward is calculated. Reinforcement learning is based on training one or more software agents to select actions that increase the cumulative reward, thereby making the software agent perform better on a given task (evidence is the increase in reward).

[0170] In addition, some techniques can be applied to some machine learning algorithms. For example, feature learning can be used. In other words, a machine learning model can be trained at least in part using feature learning, and / or a machine learning algorithm can include a feature learning component. Feature learning algorithms (which can be called representation learning algorithms) can retain the information in their input, but can also transform it in a useful way, usually as a preprocessing step before performing classification or prediction. For example, feature learning can be based on principal component analysis or cluster analysis.

[0171] In some examples, anomaly detection (i.e., outlier detection) can be used, the goal of which is to identify input values ​​that are sufficiently different from the majority of inputs or training data to be suspicious. In other words, a machine learning model can be trained at least in part using anomaly detection, and / or a machine learning algorithm can include an anomaly detection component.

[0172] In some examples, a machine learning algorithm can use a decision tree as a prediction model. In other words, a machine learning model can be based on a decision tree. In a decision tree, an observation about an item (e.g., a set of input values) can be represented by a branch of the decision tree, and an output value corresponding to the item can be represented by a leaf of the decision tree. A decision tree can support both discrete values ​​and continuous values ​​as output values. If discrete values ​​are used, the decision tree can be represented as a classification tree, and if continuous values ​​are used, the decision tree can be represented as a regression tree.

[0173] Association rules are another technique that can be used with machine learning algorithms. In other words, a machine learning model can be based on one or more association rules. Association rules are created by identifying relationships between variables in a large amount of data. Machine learning algorithms can identify and / or utilize one or more relationship rules that represent knowledge derived from the data. These rules can be used to store, manipulate, or apply knowledge.

[0174] Machine learning algorithms are often based on machine learning models. In other words, the term "machine learning algorithm" may refer to a set of instructions that can be used to create, train, or use a machine learning model. The term "machine learning model" may refer to a data structure and / or a rule set that represents the learned knowledge (e.g., based on the training performed by the machine learning algorithm). In an embodiment, the use of a machine learning algorithm may mean the use of an underlying machine learning model (or multiple underlying machine learning models). The use of a machine learning model may mean that the machine learning model and / or the data structure / rule set that is the machine learning model is trained by a machine learning algorithm.

[0175] For example, a machine learning model can be an artificial neural network (ANN). ANN is a system inspired by biological neural networks, such as neural networks in the retina or brain. ANN includes multiple interconnected nodes and multiple connections between nodes, so-called edges. There are usually three types of nodes: input nodes that receive input values, hidden nodes that are (only) connected to other nodes, and output nodes that provide output values. Each node can represent an artificial neuron. Each edge can transfer information from one node to another. The output of a node can be defined as a (non-linear) function of its input (e.g. the sum of its inputs). The input of a node can be used in a function based on the "weight" of the edge or the node providing the input. The weights of the nodes and / or edges can be adjusted during the learning process. In other words, the training of an artificial neural network can include adjusting the weights of the nodes and / or edges of the artificial neural network, i.e. achieving the desired output for a given input.

[0176] Alternatively, the machine learning model can be a support vector machine, a random forest model or a gradient boosting model. A support vector machine (i.e., a support vector network) is a supervised learning model with a related learning algorithm, which can be used to analyze data (e.g., in classification or regression analysis). A support vector machine can be trained by providing an input with multiple training input values ​​belonging to one of two categories. A support vector machine can be trained to assign new input values ​​to one of two categories. Alternatively, the machine learning model can be a Bayesian network, which is a probabilistic directed acyclic graph model. A Bayesian network can use a directed acyclic graph to represent a set of random variables and their conditional dependencies. Alternatively, the machine learning model can be based on a genetic algorithm, which is a search algorithm and heuristic technique that mimics a natural selection process.

[0177] Reference numerals list

[0178] 100 Focus Image System

[0179] 101 Multi-aperture stage focusing image

[0180] 102 The measured focus is within the limit

[0181] 104 The measured focus is at the position of the limit

[0182] 106 Measurement sequence

[0183] 108 Measurement sequence

[0184] 112 First focus range

[0185] 114 Focus Limit

[0186] 116 Focus lower limit

[0187] 118 Measurement Focus

[0188] 120 Second Focus

[0189] 122 Measured focus level

[0190] 200 Focusing Image System

[0191] 201 Single sample focus image

[0192] 210 samples

[0193] 220 Holes in the sample

[0194] 300 Extension Functions

[0195] 302 Extended focus range

[0196] 304 Focus range extension

[0197] 400 Classification Function

[0198] 402 Focus Extension

[0199] 404a Second focus of the first measurement

[0200] 404b Unwanted Object

[0201] 406a Second focus of the second measurement

[0202] 406b Object of Interest

[0203] 500 Classification Function

[0204] 502 Second focus of the first measurement

[0205] 504 Second Focus of Second Measurement

[0206] 600 Classification Function

[0207] 602a Object of interest

[0208] 602b Reduced focus range

[0209] 700 Analysis System

[0210] 710 Microscope

[0211] 720 Computer

Claims

1. A computer-based method for measuring multiple optical focuses comprising the following steps: - measuring a first focus (102, 118); - Obtain one or both of the following: a) a focus range (112) for measuring a second focus, wherein the focus range is based on the first focus; b) a starting value (118) for measuring a second focus, wherein the starting value is based on the first focus; - measuring said second focus (104) using the obtained focus range (112) and / or the starting value (118); - if the second focus is measured as an extreme value of the focus range (122), discarding the second focus (104); -Measure the third focus.

2. The method according to the preceding claim, wherein: The first focusing and the second focusing and / or the third focusing are different in the wells of the multi-well plate (101).

3. A method according to any one of the preceding claims, wherein: Within a single sample, in particular a tissue sample and / or a liquid sample, the first focus and the second focus and / or the third focus differ in a position perpendicular to the focus dimension.

4. The method according to any one of the preceding claims, comprising the steps of: - identifying one or more predetermined structures (404b, 406b); and Wherein, the first focusing, the second focusing and / or the third focusing are based on one or more of the predetermined structures.

5. The method according to any one of the preceding claims, comprising the steps of: - identifying one or more predetermined structures (404b, 406b, 602a); and Wherein, the focusing range (602b) is based on the predetermined structure.

6. The method according to any one of claims 4 to 5, wherein: The one or more predetermined structures (404b, 406b, 602a) are identified based on a classifier, in particular a machine learning algorithm.

7. A method according to any one of the preceding claims, wherein: The center of the focus range of the second focus (120) is equal to the first focus (118).

8. The method according to any one of the preceding claims, comprising the steps of: - if the second focus is measured as an extreme value (122) of the focus range (120) and before discarding the second focus, adjusting the focus range.

9. The method according to claim 8, wherein: The focus range (120) is extended on one side (304) of the focus range, in particular on the side of the focus range where the second focus has been identified as an extreme value, or on both sides of the focus range.

10. The method according to any one of claims 7 to 9, comprising the following steps: - if the second focus is measured as an extreme value of the extended focus range (304), and the focus score of the second focus of the extended focus range is higher than the focus score of the second focus in the non-extended focus range, extending the focus range again (120).

11. The method according to any one of the preceding claims, comprising one or both of the following steps: - determining the second focus based on non-discarded focuses preceding the second focus, in particular the first focus (120); - Determining the second focus (120) based on a non-discarded focus subsequent to the second focus, in particular the third focus.

12. The method according to claim 11, comprising the steps of: - Determining said second focus (120) based on a non-discarded focus measured at a position or aperture closest to said second focus.

13. The method according to any one of the preceding claims, comprising the steps of: - obtaining one or more of a starting value or a focus range for measuring the third focus, wherein, if the second focus is discarded, the corresponding parameter is based on a focus before the second focus, in particular the first focus.

14. The method according to any one of the preceding claims, comprising the steps of: - obtaining one or more of said starting value or said focus range for measuring said third focus, wherein, if said second focus is discarded, the corresponding parameter is based on a focus subsequent to said second focus.

15. A computer-based method for measuring a plurality of optical focuses of a focus map, comprising the steps of: - obtaining a plurality of positions (101), wherein at each of said positions a measurement is focused; - measuring a first focus at the obtained first position; - obtaining one or more requirements for a second focus (120) to be measured at a second position, wherein the requirements include at least one of the following: a) the first focused information (112, 118); b) information about one or more acceptable objects (406b) of said second focus; - discarding said second focus (120) if said second focus is measured to be at the extreme of its focus range (122), and / or if said second focus does not include an acceptable object (406b).

16. A medical device, in particular a microscope, having an autofocus device, which is configured as follows: - performing a method according to any one of the preceding claims; or - communicating with a device executing the method according to any of the preceding claims, in particular communicating with said device via an application user interface.

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